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A Novel Fuzzy Algorithm to Introduce New Variables in the Drug Supply Decision-Making Process in Medicine

Author

Listed:
  • Jose M. Gonzalez-Cava
  • José Antonio Reboso
  • José Luis Casteleiro-Roca
  • José Luis Calvo-Rolle
  • Juan Albino Méndez Pérez
Abstract
One of the main challenges in medicine is to guarantee an appropriate drug supply according to the real needs of patients. Closed-loop strategies have been widely used to develop automatic solutions based on feedback variables. However, when the variable of interest cannot be directly measured or there is a lack of knowledge behind the process, it turns into a difficult issue to solve. In this research, a novel algorithm to approach this problem is presented. The main objective of this study is to provide a new general algorithm capable of determining the influence of a certain clinical variable in the decision making process for drug supply and then defining an automatic system able to guide the process considering this information. Thus, this new technique will provide a way to validate a given physiological signal as a feedback variable for drug titration. In addition, the result of the algorithm in terms of fuzzy rules and membership functions will define a fuzzy-based decision system for the drug delivery process. The method proposed is based on a Fuzzy Inference System whose structure is obtained through a decision tree algorithm. A four-step methodology is then developed: data collection, preprocessing, Fuzzy Inference System generation, and the validation of results. To test this methodology, the analgesia control scenario was analysed. Specifically, the viability of the Analgesia Nociception Index (ANI) as a guiding variable for the analgesic process during surgical interventions was studied. Real data was obtained from fifteen patients undergoing cholecystectomy surgery.

Suggested Citation

  • Jose M. Gonzalez-Cava & José Antonio Reboso & José Luis Casteleiro-Roca & José Luis Calvo-Rolle & Juan Albino Méndez Pérez, 2018. "A Novel Fuzzy Algorithm to Introduce New Variables in the Drug Supply Decision-Making Process in Medicine," Complexity, Hindawi, vol. 2018, pages 1-15, February.
    • Handle: RePEc:hin:complx:9012720
    DOI: 10.1155/2018/9012720
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    References listed on IDEAS

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    1. Michał Jakubczyk & Bogumił Kamiński, 2017. "Fuzzy approach to decision analysis with multiple criteria and uncertainty in health technology assessment," Annals of Operations Research, Springer, vol. 251(1), pages 301-324, April.
    2. Pomerol, Jean-Charles, 1997. "Artificial intelligence and human decision making," European Journal of Operational Research, Elsevier, vol. 99(1), pages 3-25, May.
    3. Wang, Zeyu & Srinivasan, Ravi S., 2017. "A review of artificial intelligence based building energy use prediction: Contrasting the capabilities of single and ensemble prediction models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 796-808.
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